Equivariant Learning of Stochastic Fields:
Gaussian Processes and Steerable Conditional Neural Processes
Peter Holderrieth * 1 Michael Hutchinson * 1 Yee Whye Teh 1 2
Abstract
Motivated by objects such as electric fields or
fluid streams, we study the problem of learning
stochastic fields, i.e. stochastic processes whose
samples are fields like those occurring in physics
and engineering. Considering general transforma-
tions such as rotation ...


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